Cyberattacks Predictions Workflow using Machine Learning
This research aims to validate the effectiveness of a machine learning model composed of three classifiers: decision tree, logistic regression, and support vector machines. Through the design of a workflow, we demonstrate the effectiveness of the model. First, we execute a network attack, and then m...
- Autores:
-
Barrera Pérez, Carlos Eduardo
Serrano, Jairo E.
Martinez-Santos, Juan Carlos
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12335
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12335
- Palabra clave:
- Denial-Of-Service Attack;
DDoS;
Attack
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Cyberattacks Predictions Workflow using Machine Learning |
| title |
Cyberattacks Predictions Workflow using Machine Learning |
| spellingShingle |
Cyberattacks Predictions Workflow using Machine Learning Denial-Of-Service Attack; DDoS; Attack LEMB |
| title_short |
Cyberattacks Predictions Workflow using Machine Learning |
| title_full |
Cyberattacks Predictions Workflow using Machine Learning |
| title_fullStr |
Cyberattacks Predictions Workflow using Machine Learning |
| title_full_unstemmed |
Cyberattacks Predictions Workflow using Machine Learning |
| title_sort |
Cyberattacks Predictions Workflow using Machine Learning |
| dc.creator.fl_str_mv |
Barrera Pérez, Carlos Eduardo Serrano, Jairo E. Martinez-Santos, Juan Carlos |
| dc.contributor.author.none.fl_str_mv |
Barrera Pérez, Carlos Eduardo Serrano, Jairo E. Martinez-Santos, Juan Carlos |
| dc.subject.keywords.spa.fl_str_mv |
Denial-Of-Service Attack; DDoS; Attack |
| topic |
Denial-Of-Service Attack; DDoS; Attack LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
This research aims to validate the effectiveness of a machine learning model composed of three classifiers: decision tree, logistic regression, and support vector machines. Through the design of a workflow, we demonstrate the effectiveness of the model. First, we execute a network attack, and then monitoring, processing, storage, visualization, and data transfer tools are implemented to create the most realistic environment possible and obtain more accurate predictions. © 2021 IEEE. |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2023-07-21T16:23:36Z |
| dc.date.available.none.fl_str_mv |
2023-07-21T16:23:36Z |
| dc.date.submitted.none.fl_str_mv |
2023 |
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http://purl.org/coar/version/c_b1a7d7d4d402bcce |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/draft |
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http://purl.org/coar/resource_type/c_6501 |
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draft |
| dc.identifier.citation.spa.fl_str_mv |
Pérez, C. E. B., Serrano, J. E., & Martinez-Santos, J. C. (2021, December). Cyberattacks Predictions Workflow using Machine Learning. In 2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) (pp. 1-6). IEEE. |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/12335 |
| dc.identifier.doi.none.fl_str_mv |
10.1109/ICMLANT53170.2021.9690527 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Pérez, C. E. B., Serrano, J. E., & Martinez-Santos, J. C. (2021, December). Cyberattacks Predictions Workflow using Machine Learning. In 2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) (pp. 1-6). IEEE. 10.1109/ICMLANT53170.2021.9690527 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/12335 |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
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http://purl.org/coar/access_right/c_abf2 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
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openAccess |
| dc.format.extent.none.fl_str_mv |
6 páginas |
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application/pdf |
| dc.publisher.place.spa.fl_str_mv |
Cartagena de Indias |
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Proceedings of the 2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021 |
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Universidad Tecnológica de Bolívar |
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Barrera Pérez, Carlos Eduardoc0823acd-aa15-4c11-b009-58aa1307302eSerrano, Jairo E.858ddcba-7133-4518-bcf0-140fd228d579Martinez-Santos, Juan Carlos5c958644-c78d-401d-8ba9-bbd39fe773182023-07-21T16:23:36Z2023-07-21T16:23:36Z20212023Pérez, C. E. B., Serrano, J. E., & Martinez-Santos, J. C. (2021, December). Cyberattacks Predictions Workflow using Machine Learning. In 2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) (pp. 1-6). IEEE.https://hdl.handle.net/20.500.12585/1233510.1109/ICMLANT53170.2021.9690527Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThis research aims to validate the effectiveness of a machine learning model composed of three classifiers: decision tree, logistic regression, and support vector machines. Through the design of a workflow, we demonstrate the effectiveness of the model. First, we execute a network attack, and then monitoring, processing, storage, visualization, and data transfer tools are implemented to create the most realistic environment possible and obtain more accurate predictions. © 2021 IEEE.6 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Proceedings of the 2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021Cyberattacks Predictions Workflow using Machine Learninginfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Denial-Of-Service Attack;DDoS;AttackLEMBCartagena de IndiasEkanayake, N., Karunarathna, H., Miyuranga, R. (2020) What Is Cybersecurity: The Reality of Modern Threats, 1.Ibor, A., Obidinnu, J. System hardening architecture for safer access to critical business data (2015) Nigerian Journal of Technology, 34 (10), p. 788. Cited 3 times.Tripathi, N., Mehtre, B. (2013) Dos and Ddos Attacks: Impact, Analysis and Countermeasures, 12, pp. 1-6. Cited 17 times.Mijwil, M. (2015) History of Artificial Intelligence, 3, pp. 1-8. Cited 2 times. 04Choi, R.Y., Coyner, A.S., Kalpathy-Cramer, J., Chiang, M.F., Peter Campbell, J. Introduction to machine learning, neural networks, and deep learning (2020) Translational Vision Science and Technology, 9 (2), art. no. 14. Cited 185 times. http://tvst.arvojournals.org/article.aspx?articleid=2762344 doi: 10.1167/tvst.9.2.14Herńandez, J., Cajamarca, W. (2020) Modelos de Clasificación de Ataques de Reflexión DDoS Usando Técnicas de Machine LearningHerńandez, J., Cajamarca, W. (2020) Modelos de Clasificación de Ataques de Reflexión DDoS Usando Técnicas de Machine LearningÁlvarez Almeida, L., Martinez-Santos, J.C. SIDS-DDoS, a Smart Intrusion Detection System for Distributed Denial of Service Attacks (2020) Advances in Intelligent Systems and Computing, 1067, pp. 380-389. http://www.springer.com/series/11156 ISBN: 978-303032032-4 doi: 10.1007/978-3-030-32033-1_35Banerjee, U., Vashishtha, A., Saxena, M. Evaluation of the capabilities of wireshark as a tool for intrusion detection (2010) International Journal of Computer Applications, 6 (7), pp. 1-5. Cited 38 times.Banerjee, U., Vashishtha, A., Saxena, M. Evaluation of the capabilities of wireshark as a tool for intrusion detection (2010) International Journal of Computer Applications, 6 (7), pp. 1-5. Cited 38 times.Bajer, M. Building an IoT data hub with elasticsearch, Logstash and Kibana (2017) Proceedings - 2017 5th International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2017, 2017-January, pp. 63-68. Cited 56 times. ISBN: 978-153863281-9 doi: 10.1109/FiCloudW.2017.101Sanjappa, S., Ahmed, M. Analysis of logs by using logstash (2017) Advances in Intelligent Systems and Computing, 516, pp. 579-585. Cited 14 times. http://www.springer.com/series/11156 ISBN: 978-981103155-7 doi: 10.1007/978-981-10-3156-4_61Gormley, C., Tong, Z. (2015) Elasticsearch: The Definitive Guide: A Distributed Real-time Search and Analytics Engine.. Cited 395 times. "O'Reilly Media, Inc."Kúc, R., Rogozinski, M. (2016) ElasticSearch Server. Cited 25 times. Packt Publishing LtdAzarmi, B. (2017) Learning Kibana 5.0. Cited 3 times. Packt Publishing LtdSharafaldin, I., Lashkari, A.H., Hakak, S., Ghorbani, A.A. Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy (2019) Proceedings - International Carnahan Conference on Security Technology, 2019-October, art. no. 8888419. Cited 359 times. ISBN: 978-172811576-4 doi: 10.1109/CCST.2019.8888419Álvarez Almeida, L.A. Data model classification based on machine learning techniques for detection of anomalous traffic (2019) Cartagena de IndiasAgnihotri, J., Phalnikar, R. Development of performance testing suite using apache JMeter (2018) Advances in Intelligent Systems and Computing, 673, pp. 317-326. 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